## File: Rglpk_solve.Rd

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rglpk 0.6-4-1
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 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158 \name{Rglpk_solve_LP} \alias{Rglpk_solve_LP} \title{Linear and Mixed Integer Programming Solver Using GLPK} \description{ High level R interface to the GNU Linear Programming Kit (GLPK) for solving linear as well as mixed integer linear programming (MILP) problems. } \usage{ Rglpk_solve_LP(obj, mat, dir, rhs, bounds = NULL, types = NULL, max = FALSE, control = list(), \ldots) } \arguments{ \item{obj}{a numeric vector representing the objective coefficients.} \item{mat}{a numeric vector or a (sparse) matrix of constraint coefficients. If the optimization problem is unconstrained then a matrix of dimension 0 times the number of objective variables is required.} \item{dir}{a character vector with the directions of the constraints. For a nonzero number of constraints each element must be one of \code{"<"}, \code{"<="}, \code{">"}, \code{">="}, or \code{"=="}. Note, however, that the GLPK API only allows for non-strict inequalities. Strict inequalities are handled the same way as non-strict inequalities.} \item{rhs}{a numeric vector representing the right hand side of the constraints.} \item{bounds}{\code{NULL} (default) or a list with elements \code{upper} and \code{lower} containing the indices and corresponding bounds of the objective variables. The default for each variable is a bound between 0 and \code{Inf}.} \item{types}{a character vector indicating the types of the objective variables. \code{types} can be either \code{"B"} for binary, \code{"C"} for continuous or \code{"I"} for integer. By default \code{NULL}, taken as all-continuous. Recycled as needed.} \item{max}{a logical giving the direction of the optimization. \code{TRUE} means that the objective is to maximize the objective function, \code{FALSE} (default) means to minimize it.} \item{control}{a list of parameters to the solver. See *Details*.} \item{\ldots}{a list of control parameters (overruling those specified in \code{control}).} } \details{ GLPK is open source. The current version can be found at \url{https://www.gnu.org/software/glpk/glpk.html}. Package \pkg{Rglpk} provides a high level solver function using the low level C interface of the GLPK solver. R interface packages which port all low level C routines of the GLPK API to R are also available. Consult the \sQuote{See Also} Section for references. Matrix \code{mat} and \code{obj} may be sparse arrays or matrices (\code{simple_triplet_matrix}) as provided by the \pkg{slam} package. The \code{control} argument can be used to set GLPK's many parameters. See the respective method section of the \cite{GNU Linear Programming Kit Reference Manual} for further details. The following parameters are supported: \describe{ \item{verbose:}{turn GLPK terminal output on (\code{TRUE}) or off (\code{FALSE}, the default).} \item{presolve:}{turn presolver on (\code{TRUE}) or off (\code{FALSE}, the default).} \item{tm_limit:}{time limit in milliseconds of call to optimizer. Can be any nonnegative integer. Default: 0 (use GLPK default).} \item{canonicalize_status:}{a logical indicating whether to canonicalize GLPK status codes (on success \code{Rglpk_solve_LP()} returns code 0) or not (1). Default: \code{TRUE}.}} } \value{ A list containing the optimal solution, with the following components. \item{solution}{the vector of optimal coefficients} \item{objval}{the value of the objective function at the optimum} \item{status}{an integer with status information about the solution returned. If the control parameter \code{canonicalize_status} is set (the default) then it will return 0 for the optimal solution being found, and non-zero otherwise. If the control parameter is set to \code{FALSE} it will return the GLPK status codes.} \item{solution_dual}{variable reduced cost, if available (\code{NA} otherwise).} \item{auxiliary}{a list with two vectors each containing the values of the auxiliary variable associated with the respective constraint at solution, primal and dual (if available, \code{NA} otherwise).} } \references{ GNU Linear Programming Kit (\url{https://www.gnu.org/software/glpk/glpk.html}). GLPK Interface to R (\url{https://cran.R-project.org/package=Rglpk}). } \author{Stefan Theussl and Kurt Hornik} \seealso{ \pkg{glpk} and \pkg{glpkAPI} for C API bindings; \code{\link[lpSolve]{lp}} in package \pkg{lpSolve}; \code{\link[ROI]{ROI_solve}} in package \pkg{ROI}; \code{\link[Rsymphony]{Rsymphony_solve_LP}} in package \pkg{Rsymphony}. } \examples{ ## Simple linear program. ## maximize: 2 x_1 + 4 x_2 + 3 x_3 ## subject to: 3 x_1 + 4 x_2 + 2 x_3 <= 60 ## 2 x_1 + x_2 + 2 x_3 <= 40 ## x_1 + 3 x_2 + 2 x_3 <= 80 ## x_1, x_2, x_3 are non-negative real numbers obj <- c(2, 4, 3) mat <- matrix(c(3, 2, 1, 4, 1, 3, 2, 2, 2), nrow = 3) dir <- c("<=", "<=", "<=") rhs <- c(60, 40, 80) max <- TRUE Rglpk_solve_LP(obj, mat, dir, rhs, max = max) ## Simple mixed integer linear program. ## maximize: 3 x_1 + 1 x_2 + 3 x_3 ## subject to: -1 x_1 + 2 x_2 + x_3 <= 4 ## 4 x_2 - 3 x_3 <= 2 ## x_1 - 3 x_2 + 2 x_3 <= 3 ## x_1, x_3 are non-negative integers ## x_2 is a non-negative real number obj <- c(3, 1, 3) mat <- matrix(c(-1, 0, 1, 2, 4, -3, 1, -3, 2), nrow = 3) dir <- c("<=", "<=", "<=") rhs <- c(4, 2, 3) types <- c("I", "C", "I") max <- TRUE Rglpk_solve_LP(obj, mat, dir, rhs, types = types, max = max) ## Same as before but with bounds replaced by ## -Inf < x_1 <= 4 ## 0 <= x_2 <= 100 ## 2 <= x_3 < Inf bounds <- list(lower = list(ind = c(1L, 3L), val = c(-Inf, 2)), upper = list(ind = c(1L, 2L), val = c(4, 100))) Rglpk_solve_LP(obj, mat, dir, rhs, bounds, types, max) ## Examples from the GLPK manual ## Solver output enabled ## 1.3.1 ## maximize: 10 x_1 + 6 x_2 + 4 x_3 ## subject to: x_1 + x_2 + x_3 <= 100 ## 10 x_1 + 4 x_2 + 5 x_3 <= 600 ## 2 x_1 + 2 x_2 + 6 x_3 <= 300 ## x_1, x_2, x_3 are non-negative real numbers obj <- c(10, 6, 4) mat <- matrix(c(1, 10, 2, 1, 4, 2, 1, 5, 6), nrow = 3) dir <- c("<=", "<=", "<=") rhs <- c(100, 600, 300) max <- TRUE Rglpk_solve_LP(obj, mat, dir, rhs, max = max, control = list("verbose" = TRUE, "canonicalize_status" = FALSE)) } \keyword{optimize}